tmp_project
Jill Castellano
2024-12-14
Questions about and limitations with the data
–how many people have answered in both surveys?
–jails with less
than 5 responses are suppressed, so must use nationwide tibbles to get
nationwide totals
–survey responses are not representative
–data is not broken down on individual level but aggregated to the level
of each jail
–number of respondents in any given jail to these
questions are pretty small
Setup
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# import all csv files in data folder into this project
data_folder <- "../data/"
# list all csv files in the folder
csv_files <- list.files(path = data_folder, pattern = "\\.csv$", full.names = TRUE)
# import each csv as a tibble in data_frames list
data_frames <- csv_files %>%
set_names(~ str_remove(basename(.), "\\.csv$")) %>% # remove .csv from filenames
map(~ as.data.frame(read_csv(.)))## Rows: 3 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): eligible_to_vote
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 379 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, eligible_to_vote
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 445 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, gender
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 3 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): people_with_convictions_should_vote
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 22 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, people_with_convictions_should_vote
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 6 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): sentence_length
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 26 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, sentence_length
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 5 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): skills_for_politics
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 418 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, skills_for_politics
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 457 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, us_ready_elect_woman_president
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 4 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): vote_impact_government_level
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 385 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, vote_impact_government_level
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 5 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): who_vote_for
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 6 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): who_vote_for_supplement
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 423 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, who_vote_for_supplement
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 373 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): state, answer_set, facility_name, who_vote_for
## dbl (3): count, n_respondents, pct_of_respondents
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# add each tibble as a data frame in the global environment
list2env(data_frames, envir = .GlobalEnv)## <environment: R_GlobalEnv># print names of the imported data frames
print(names(data_frames))## [1] "eligible_to_vote_nationwide"
## [2] "eligible_to_vote"
## [3] "gender"
## [4] "people_with_convictions_nationwide"
## [5] "people_with_convictions"
## [6] "sentence_length_nationwide"
## [7] "sentence_length"
## [8] "skills_for_politics_nationwide"
## [9] "skills_for_politics"
## [10] "us_ready_elect_woman_president"
## [11] "vote_impact_government_level_nationwide"
## [12] "vote_impact_government_level"
## [13] "who_vote_for_nationwide"
## [14] "who_vote_for_supplement_nationwide"
## [15] "who_vote_for_supplement"
## [16] "who_vote_for"Voting Eligibility
# how many respondents are not sure of their eligibility to vote?
head(eligible_to_vote)## state answer_set facility_name eligible_to_vote
## 1 IL all-surveys Cook County, IL Yes
## 2 CA all-surveys Santa Clara County, CA Yes
## 3 AZ all-surveys Maricopa County Sheriff's Office, AZ No
## 4 AZ all-surveys Maricopa County Sheriff's Office, AZ Yes
## 5 CA all-surveys Sacramento County Sheriff's Office, CA Yes
## 6 AZ all-surveys Maricopa County Sheriff's Office, AZ Not sure
## count n_respondents pct_of_respondents
## 1 356 418 0.8516746
## 2 238 347 0.6858790
## 3 211 505 0.4178218
## 4 164 505 0.3247525
## 5 130 233 0.5579399
## 6 129 505 0.2554455head(eligible_to_vote_nationwide)## eligible_to_vote count n_respondents pct_of_respondents
## 1 Yes 4165 8030 0.5186800
## 2 Not sure 1676 8030 0.2087173
## 3 No 1585 8030 0.1973848# 1,676 incarcerated people (20.8% of survey respondents) not sure about eligibility to vote
# how does this break down for each jail?
not_sure_eligibility <- eligible_to_vote %>%
filter(eligible_to_vote == "Not sure") %>% # filter rows where eligible_to_vote is "Not sure"
arrange(desc(pct_of_respondents)) %>% # sort by pct_of_respondents
select(state,facility_name,count,n_respondents,pct_of_respondents) #select columns of interest
head(not_sure_eligibility)## state facility_name count n_respondents
## 1 NM Eddy County Detention Center, NM 10 19
## 2 MA Berkshire County House of Corrections, MA 10 20
## 3 SC Clarendon County, SC 6 13
## 4 NC Durham County NC - Detention Facility 9 20
## 5 PA Washington County, PA 19 43
## 6 NJ Ocean County, NJ 7 16
## pct_of_respondents
## 1 0.5263158
## 2 0.5000000
## 3 0.4615385
## 4 0.4500000
## 5 0.4418605
## 6 0.4375000not_sure_eligibility %>%
filter(n_respondents > 50)## state facility_name count n_respondents
## 1 TX Potter County Detention, TX 20 52
## 2 WA Yakima County, WA 29 82
## 3 TX Guadalupe County, TX 18 52
## 4 IN Hamilton County Main Jail, IN 22 65
## 5 CA Orange County, CA - Theo Lacy 31 93
## 6 OH Mahoning County Justice Center, OH 25 75
## 7 WA Spokane County Jail, WA 24 72
## 8 TX Travis County Jail, TX 53 162
## 9 GA Athens Clarke County Jail, GA 17 52
## 10 PA Lackawana County, PA 27 84
## 11 IL Winnebago County, IL 34 108
## 12 MI Macomb County, MI 17 54
## 13 CA Sacramento County Sheriff's Office, CA 70 233
## 14 NC Mecklenburg County Sheriffs Office, NC 20 67
## 15 PA Westmoreland County, PA 17 57
## 16 CA Orange County, CA - Central Jail Complex 20 68
## 17 TX Collin County, TX 15 51
## 18 VA Virginia Beach Correctional Facility, VA 23 80
## 19 AZ Pima County Jail, AZ 42 149
## 20 CA Stanislaus County, CA 38 142
## 21 CA San Joaquin County Jail, CA 20 78
## 22 AZ Maricopa County Sheriff's Office, AZ 129 505
## 23 NY Suffolk County, NY 13 51
## 24 CA Ventura County Sheriff's Office, CA 18 72
## 25 TX McLennan County, TX 14 57
## 26 CO Weld County, CO 21 88
## 27 NC Cumberland County, NC 17 72
## 28 CA Fresno County, CA 38 166
## 29 PA Dauphin County Jail, PA 19 84
## 30 NY Monroe County Sheriff's Department, NY 14 62
## 31 NY Albany County Correctional Facility, NY 16 71
## 32 OH Cuyahoga County Justice Center, OH 34 151
## 33 PA Lehigh County, PA 13 58
## 34 VA Chesapeake Correctional Center, VA 14 64
## 35 GA Gwinnett County Sheriffs Office, GA 17 78
## 36 MA Essex County Middleton HOC, MA 13 60
## 37 CA Santa Barbara County, CA 11 51
## 38 CO Mesa County, CO 12 59
## 39 PA Montgomery County Correctional Facility, PA 11 56
## 40 CA Santa Clara County, CA 64 347
## 41 MO St. Louis County Jail, MO 12 74
## 42 MA Middlesex County House of Corrections, MA 8 53
## 43 MI Kent County, MI 10 72
## 44 MA Worcester County Jail, MA 7 51
## 45 MA Suffolk County Jail, MA 8 60
## 46 CO Pueblo County, CO 8 63
## 47 CO Denver County Jail, CO 11 103
## 48 IL Cook County, IL 32 418
## pct_of_respondents
## 1 0.38461538
## 2 0.35365854
## 3 0.34615385
## 4 0.33846154
## 5 0.33333333
## 6 0.33333333
## 7 0.33333333
## 8 0.32716049
## 9 0.32692308
## 10 0.32142857
## 11 0.31481481
## 12 0.31481481
## 13 0.30042918
## 14 0.29850746
## 15 0.29824561
## 16 0.29411765
## 17 0.29411765
## 18 0.28750000
## 19 0.28187919
## 20 0.26760563
## 21 0.25641026
## 22 0.25544554
## 23 0.25490196
## 24 0.25000000
## 25 0.24561404
## 26 0.23863636
## 27 0.23611111
## 28 0.22891566
## 29 0.22619048
## 30 0.22580645
## 31 0.22535211
## 32 0.22516556
## 33 0.22413793
## 34 0.21875000
## 35 0.21794872
## 36 0.21666667
## 37 0.21568627
## 38 0.20338983
## 39 0.19642857
## 40 0.18443804
## 41 0.16216216
## 42 0.15094340
## 43 0.13888889
## 44 0.13725490
## 45 0.13333333
## 46 0.12698413
## 47 0.10679612
## 48 0.07655502# These facilities in texas, washington and Indiana could be interesting to contact
# how do incarcerated people feel about whether they should vote?
head(people_with_convictions_nationwide)## people_with_convictions_should_vote
## 1 While incarcerated for any crime
## 2 After they have left prison
## 3 While incarcerated, but only if they were convicted of non-violent offenses
## count n_respondents pct_of_respondents
## 1 213 375 0.56800000
## 2 95 375 0.25333333
## 3 22 375 0.05866667# 57% of respondents said they should be able to vote while incarcerated
# note - question is worded "after they left prison" but these respondents are in jail, not prison
# looks like there was no answer option like "they should never be allowed to vote"
head(people_with_convictions) %>%
filter(people_with_convictions_should_vote == "While incarcerated for any crime") %>%
arrange(desc(pct_of_respondents))## state answer_set facility_name
## 1 TX all-surveys MTC: Giles Dalby, TX
## 2 WV all-surveys WV DCR North Central Regional Jail
## 3 WV all-surveys WV DCR Southwestern Regional Jail
## 4 WV all-surveys WV DCR Tygart Valley Regional Jail
## people_with_convictions_should_vote count n_respondents pct_of_respondents
## 1 While incarcerated for any crime 57 89 0.6404494
## 2 While incarcerated for any crime 47 74 0.6351351
## 3 While incarcerated for any crime 22 36 0.6111111
## 4 While incarcerated for any crime 19 40 0.4750000#interesting that three of the four jails listed here are in West Virginia
head(vote_impact_government_level_nationwide)## vote_impact_government_level count n_respondents pct_of_respondents
## 1 State 2100 7082 0.29652640
## 2 City / Town 2025 7082 0.28593618
## 3 Federal 1660 7082 0.23439706
## 4 County 605 7082 0.08542785# about 30% think biggest impact will be at the state levelVoting in 2024 Election
# compare likelihood of voting for major candidates in first and second survey
head(who_vote_for_nationwide) #56% for Trump in first survey, 9.6% for Biden## who_vote_for count n_respondents pct_of_respondents
## 1 Donald J. Trump 4708 8385 0.56147883
## 2 Joe Biden 810 8385 0.09660107
## 3 Wouldn't vote 700 8385 0.08348241
## 4 Third-Party Candidate 425 8385 0.05068575
## 5 Don't know 383 8385 0.04567680head(who_vote_for_supplement_nationwide) #52% for Trump second survey, 24.6% for Harris## who_vote_for_supplement count n_respondents pct_of_respondents
## 1 Donald J. Trump 4381 8396 0.5217960934
## 2 Kamala Harris 2068 8396 0.2463077656
## 3 Don't know 342 8396 0.0407336827
## 4 Wouldn't vote 265 8396 0.0315626489
## 5 Third-party candidate 43 8396 0.0051214864
## 6 Donald J. Trump, Kamala Harris 8 8396 0.0009528347# interestingly, a LOT more support for Harris than for Biden
# compare across jails for jails with more respondents (more than 50)
# what percent of respondents said they would vote for biden in the first survey?
biden_vote <- who_vote_for %>%
filter(who_vote_for == "Joe Biden", # filter rows for Biden voters
n_respondents > 50) %>% # filter for jails with more than 50 respondents
arrange(desc(pct_of_respondents)) %>% # sort by pct_of_respondents
select(facility_name, pct_of_respondents) %>% # select columns of interest
rename(pct_for_biden = pct_of_respondents) # rename the column
# what percent of respondents said they would vote for harris in the second survey?
harris_vote <- who_vote_for_supplement %>%
filter(who_vote_for_supplement == "Kamala Harris", # filter rows for Harris voters
n_respondents > 50) %>% # filter for jails with more than 50 respondents
arrange(desc(pct_of_respondents)) %>% # sort by pct_of_respondents
select(facility_name,pct_of_respondents) %>% # select columns of interest
rename(pct_for_harris = pct_of_respondents) # rename the column
# join the datasets by facility_name
biden_vs_harris <- left_join(biden_vote, harris_vote, by = "facility_name")
# calculate the percentage point difference between support for harris and biden in the large facilities
biden_vs_harris <- biden_vs_harris %>%
mutate(pct_point_difference = pct_for_harris - pct_for_biden) %>%
arrange(desc(pct_point_difference)) # sort by pct_point_difference in descending order
# view data
print(biden_vs_harris)## facility_name pct_for_biden pct_for_harris
## 1 MTC: Giles Dalby, TX 0.10112360 0.3548387
## 2 Weld County, CO 0.13043478 0.3493976
## 3 Cook County, IL 0.13776722 0.3415385
## 4 Gwinnett County Sheriffs Office, GA 0.09210526 0.2807018
## 5 Montgomery County Correctional Facility, PA 0.16363636 0.3508772
## 6 Travis County Jail, TX 0.10240964 0.2888889
## 7 Kent County, MI 0.15492958 0.3392857
## 8 Stanislaus County, CA 0.09589041 0.2777778
## 9 Cuyahoga County Justice Center, OH 0.09150327 0.2720000
## 10 Pima County Jail, AZ 0.16326531 0.3333333
## 11 Pueblo County, CO 0.14062500 0.3018868
## 12 Mesa County, CO 0.13114754 0.2909091
## 13 Fresno County, CA 0.19760479 0.3538462
## 14 Lackawana County, PA 0.15116279 0.2985075
## 15 Orange County, CA - Theo Lacy 0.19565217 0.3421053
## 16 Maricopa County Sheriff's Office, AZ 0.16338583 0.3050000
## 17 Dauphin County Jail, PA 0.07142857 0.2105263
## 18 Sacramento County Sheriff's Office, CA 0.09871245 0.2280702
## 19 Hamilton County Main Jail, IN 0.12500000 0.2539683
## 20 Winnebago County, IL 0.10185185 0.2293578
## 21 Orange County, CA - Central Jail Complex 0.11594203 0.2238806
## 22 Denver County Jail, CO 0.14423077 0.2465753
## 23 Ventura County Sheriff's Office, CA 0.22222222 0.3018868
## 24 WV DCR North Central Regional Jail 0.09333333 0.1515152
## 25 Santa Clara County, CA 0.23295455 NA
## 26 Santa Barbara County, CA 0.22641509 NA
## 27 McLennan County, TX 0.21052632 NA
## 28 Suffolk County Jail, MA 0.17741935 NA
## 29 Mecklenburg County Sheriffs Office, NC 0.16923077 NA
## 30 San Joaquin County Jail, CA 0.16455696 NA
## 31 Chesapeake Correctional Center, VA 0.15625000 NA
## 32 Cumberland County, NC 0.13888889 NA
## 33 Albany County Correctional Facility, NY 0.13888889 NA
## 34 St. Louis County Jail, MO 0.13513514 NA
## 35 Athens Clarke County Jail, GA 0.13461538 NA
## 36 Middlesex County House of Corrections, MA 0.13461538 NA
## 37 Essex County Middleton HOC, MA 0.13333333 NA
## 38 Worcester County Jail, MA 0.11764706 NA
## 39 Yakima County, WA 0.11627907 NA
## 40 Mahoning County Justice Center, OH 0.11538462 NA
## 41 Suffolk County, NY 0.11538462 NA
## 42 Collin County, TX 0.11538462 NA
## 43 Potter County Detention, TX 0.11538462 NA
## 44 Monroe County Sheriff's Department, NY 0.09375000 NA
## pct_point_difference
## 1 0.25371511
## 2 0.21896281
## 3 0.20377124
## 4 0.18859649
## 5 0.18724083
## 6 0.18647925
## 7 0.18435614
## 8 0.18188737
## 9 0.18049673
## 10 0.17006803
## 11 0.16126179
## 12 0.15976155
## 13 0.15624136
## 14 0.14734467
## 15 0.14645309
## 16 0.14161417
## 17 0.13909774
## 18 0.12935773
## 19 0.12896825
## 20 0.12750595
## 21 0.10793857
## 22 0.10234457
## 23 0.07966457
## 24 0.05818182
## 25 NA
## 26 NA
## 27 NA
## 28 NA
## 29 NA
## 30 NA
## 31 NA
## 32 NA
## 33 NA
## 34 NA
## 35 NA
## 36 NA
## 37 NA
## 38 NA
## 39 NA
## 40 NA
## 41 NA
## 42 NA
## 43 NA
## 44 NA# biggest swing was in MTC Giles Dalby in Texas -- 25 percentage point difference
# future analysis - how does gender and race of respondents affect their likelihood to vote for Trump/Biden?Testing a Gender Effect
# are jails with more female respondents more likely to support kamala harris?
# new dataset just showing jails with female respondents
jails_with_women <- gender %>%
filter(gender == "Woman") %>%
select(facility_name, pct_of_respondents) %>%
arrange(desc(pct_of_respondents)) %>%
rename(pct_women = pct_of_respondents)
#new dataset just showing "yes" answers to ready for woman president question
ready_for_woman_pres <- us_ready_elect_woman_president %>%
filter(us_ready_elect_woman_president == "Yes") %>%
select(facility_name, pct_of_respondents) %>%
arrange(desc(pct_of_respondents)) %>%
rename(pct_ready_for_woman_pres = pct_of_respondents)
head(jails_with_women)## facility_name pct_women
## 1 Pulaski County, KY 0.7000000
## 2 Botetourt Craig Public Safety Facility, VA 0.5333333
## 3 Pickens County Detention Facility, SC 0.5172414
## 4 Edovo Go - General 0.4666667
## 5 Wichita County, TX 0.4545455
## 6 Venango County Prison, PA 0.4375000head(ready_for_woman_pres)## facility_name pct_ready_for_woman_pres
## 1 Livingston County Jail, NY 0.8750000
## 2 Whatcom County, WA - Main Jail 0.8125000
## 3 Catoosa County Jail, GA 0.7777778
## 4 Dakota County Jail, MN 0.7777778
## 5 Scotts Bluff County Detention Center, NE 0.7692308
## 6 Bucks County Correctional Facility, PA 0.7500000# join the datasets on facility_name
women_jails <- left_join(jails_with_women, ready_for_woman_pres, by = "facility_name")
# View the merged dataset
print(women_jails)## facility_name pct_women
## 1 Pulaski County, KY 0.70000000
## 2 Botetourt Craig Public Safety Facility, VA 0.53333333
## 3 Pickens County Detention Facility, SC 0.51724138
## 4 Edovo Go - General 0.46666667
## 5 Wichita County, TX 0.45454545
## 6 Venango County Prison, PA 0.43750000
## 7 Barnstable County Correctional Facility, MA 0.41379310
## 8 Forsyth County Detention Center, GA 0.38095238
## 9 McLennan County, TX 0.37500000
## 10 Fairfield County, OH 0.37500000
## 11 Muskingum County, OH 0.35714286
## 12 Western Regional, VA 0.35185185
## 13 Medina County Jail, OH 0.35000000
## 14 Livingston County, MI 0.34375000
## 15 Citrus County Detention Facility, FL 0.33333333
## 16 Anne Arundel County, MD 0.33333333
## 17 Yavapai County Jail, AZ 0.32432432
## 18 Columbia County Detention Center, GA 0.31250000
## 19 Iron County, UT 0.30000000
## 20 Washtenaw County Jail, MI 0.28000000
## 21 Hunt County, TX 0.28000000
## 22 Athens Clarke County Jail, GA 0.27941176
## 23 Etowah County Jail, AL 0.27777778
## 24 Pueblo County, CO 0.27659574
## 25 Eddy County Detention Center, NM 0.27586207
## 26 Onondaga County, NY 0.27272727
## 27 Warren County Jail, TN 0.26923077
## 28 Hamilton County Main Jail, IN 0.26530612
## 29 Chaves County Adult Detention Center, NM 0.26470588
## 30 Henrico County Sheriff's Office, VA 0.26086957
## 31 Sarasota County, FL 0.25757576
## 32 Brown County Jail, WI 0.25675676
## 33 Douglas Detention Center, CO 0.25581395
## 34 Larimer County, CO 0.25531915
## 35 Chesapeake Correctional Center, VA 0.25333333
## 36 Greene County, OH: Adult Detention 0.25000000
## 37 Mahoning County Justice Center, OH 0.24705882
## 38 Portage County, OH 0.24137931
## 39 Shawnee County Detention Center, KS 0.23684211
## 40 Nassau County, FL 0.23333333
## 41 Sullivan County Jail, TN 0.22727273
## 42 Lincoln County, OR 0.22500000
## 43 St. Louis County Jail, MO 0.22340426
## 44 Macomb County, MI 0.21818182
## 45 Kent County, MI 0.21000000
## 46 Madera County, CA 0.20879121
## 47 Dodge County, WI 0.20588235
## 48 Polk County, IA 0.20454545
## 49 Montgomery County Correctional Facility, PA 0.20430108
## 50 Pamunkey Regional Jail, VA 0.20338983
## 51 Santa Rosa County FL 0.20000000
## 52 Curry County Detention Center, NM 0.20000000
## 53 Sheboygan County Jail, WI 0.20000000
## 54 Pinal County Jail, AZ 0.19607843
## 55 Sonoma County, CA 0.19512195
## 56 WV DCR Central Regional Jail 0.19512195
## 57 Potter County Detention, TX 0.19480519
## 58 Hamilton County, TN 0.19148936
## 59 WV DCR North Central Regional Jail 0.19130435
## 60 Luna County Detention Center, NM 0.18918919
## 61 King County Adult Detention, WA 0.18750000
## 62 Rappahannock Regional Jail, VA 0.18666667
## 63 Onslow County Jail, NC 0.18181818
## 64 Westmoreland County, PA 0.17977528
## 65 Pinellas County Jail, FL 0.17777778
## 66 Craven County Jail, NC 0.17647059
## 67 Franklin County, PA 0.17647059
## 68 Lea County Sheriff's Office, NM 0.17142857
## 69 WV DCR Southwestern Regional Jail 0.17021277
## 70 Washington County Detention Center, MD 0.16666667
## 71 Lee County, FL 0.16393443
## 72 Escambia County Jail, FL 0.16363636
## 73 Summit County, OH 0.16326531
## 74 Whatcom County, WA - Main Jail 0.16326531
## 75 Santa Barbara County, CA 0.16216216
## 76 Mesa County, CO 0.16129032
## 77 Maricopa County Sheriff's Office, AZ 0.16118048
## 78 Johnson County, TX 0.16000000
## 79 Elkhart County Jail, IN 0.15789474
## 80 Richland County Glenn Detention Center, SC 0.15789474
## 81 San Joaquin County Jail, CA 0.15625000
## 82 Pasco County Corrections, FL 0.15540541
## 83 Cobb County, GA 0.15517241
## 84 Onondaga County-Justice Center, NY 0.15384615
## 85 Lancaster County, NE 0.14864865
## 86 Collin County, TX 0.14705882
## 87 Guadalupe County, TX 0.14473684
## 88 Sacramento County Sheriff's Office, CA 0.14429530
## 89 Orange County, CA - Central Jail Complex 0.14285714
## 90 Niagara County Correctional Facility, NY 0.14285714
## 91 Weld County, CO 0.14179104
## 92 Bristol County House of Correction, MA 0.14035088
## 93 Suffolk County, NY 0.13888889
## 94 Ventura County Sheriff's Office, CA 0.13861386
## 95 Lebanon County, PA 0.13846154
## 96 Lancaster County Prison, PA 0.13793103
## 97 Suffolk County Jail, MA 0.13698630
## 98 Yakima County, WA 0.13513514
## 99 WV DCR Tygart Valley Regional Jail 0.13333333
## 100 Cape Girardeau County Jail, MO 0.13043478
## 101 Roanoke City Adult Detention Center, VA 0.12500000
## 102 Butte County, CA 0.12280702
## 103 Kane County, IL 0.12121212
## 104 Salt Lake City County, UT 0.12121212
## 105 Okaloosa County Department of Corrections, FL 0.12068966
## 106 Santa Clara County, CA 0.11977716
## 107 Bibb County Jail, GA 0.11764706
## 108 Lackawana County, PA 0.11607143
## 109 Leon County Jail, FL 0.11340206
## 110 Mecklenburg County Sheriffs Office, NC 0.11111111
## 111 Virginia Beach Correctional Facility, VA 0.11009174
## 112 Davidson County Detention Center, TN 0.10924370
## 113 Cuyahoga County Justice Center, OH 0.10593220
## 114 Pima County Jail, AZ 0.10476190
## 115 Jefferson County Detention Facility, CO 0.10389610
## 116 Washington County, PA 0.09836066
## 117 Spokane County Jail, WA 0.09638554
## 118 Hillsborough County Sheriff's Office, FL 0.09340659
## 119 Stanislaus County, CA 0.09195402
## 120 Monroe County Sheriff's Department, NY 0.09195402
## 121 DC DOC: Central Detention Facility 0.08641975
## 122 Dauphin County Jail, PA 0.08403361
## 123 Cook County, IL 0.07557118
## 124 Denver County Jail, CO 0.07333333
## 125 Gwinnett County Sheriffs Office, GA 0.07142857
## 126 Cumberland County, NC 0.06976744
## 127 Travis County Jail, TX 0.06341463
## pct_ready_for_woman_pres
## 1 NA
## 2 NA
## 3 0.4642857
## 4 NA
## 5 NA
## 6 NA
## 7 NA
## 8 NA
## 9 0.4791667
## 10 NA
## 11 NA
## 12 0.5185185
## 13 0.6363636
## 14 0.6000000
## 15 0.2666667
## 16 NA
## 17 0.4444444
## 18 0.5882353
## 19 0.6666667
## 20 0.5000000
## 21 NA
## 22 0.4687500
## 23 0.4137931
## 24 0.6078431
## 25 0.3809524
## 26 0.6000000
## 27 0.4615385
## 28 0.5714286
## 29 NA
## 30 0.4583333
## 31 0.6060606
## 32 0.5428571
## 33 0.5333333
## 34 0.5483871
## 35 0.5937500
## 36 NA
## 37 0.5862069
## 38 0.4736842
## 39 0.5757576
## 40 0.4545455
## 41 0.4886364
## 42 0.5384615
## 43 0.5277778
## 44 NA
## 45 0.5000000
## 46 0.5312500
## 47 0.4444444
## 48 0.5384615
## 49 0.5789474
## 50 0.2857143
## 51 0.2812500
## 52 0.6000000
## 53 0.3666667
## 54 0.6363636
## 55 0.6451613
## 56 0.2258065
## 57 0.5319149
## 58 0.5434783
## 59 0.4393939
## 60 0.4000000
## 61 0.6315789
## 62 0.4210526
## 63 0.5757576
## 64 0.3870968
## 65 0.4772727
## 66 0.6000000
## 67 0.6250000
## 68 0.5769231
## 69 0.3750000
## 70 0.3666667
## 71 0.5294118
## 72 0.3214286
## 73 0.5384615
## 74 0.8125000
## 75 0.6097561
## 76 0.5272727
## 77 0.5419463
## 78 0.4545455
## 79 0.4406780
## 80 0.5000000
## 81 0.5555556
## 82 0.4081633
## 83 0.5526316
## 84 0.4090909
## 85 0.5000000
## 86 0.5000000
## 87 0.4047619
## 88 0.4957265
## 89 0.5294118
## 90 0.5217391
## 91 0.5487805
## 92 0.5384615
## 93 0.6000000
## 94 0.5094340
## 95 0.3529412
## 96 0.5087719
## 97 0.5454545
## 98 0.3953488
## 99 0.2857143
## 100 0.4500000
## 101 0.5641026
## 102 0.4736842
## 103 0.5681818
## 104 0.4687500
## 105 0.4067797
## 106 0.5937500
## 107 0.4705882
## 108 0.4242424
## 109 0.4947368
## 110 0.4130435
## 111 0.4444444
## 112 0.5000000
## 113 0.4435484
## 114 0.5272727
## 115 0.5714286
## 116 0.5135135
## 117 NA
## 118 0.3989071
## 119 0.5294118
## 120 0.4634146
## 121 0.3783784
## 122 0.3421053
## 123 0.4751553
## 124 0.4507042
## 125 0.4814815
## 126 0.5000000
## 127 0.4772727# create scatter plot of results with a line of best fit
ggplot(women_jails, aes(x = pct_women, y = pct_ready_for_woman_pres)) +
geom_point() + # scatter plot
geom_smooth(method = "lm", se = FALSE, color = "blue") + # line of best fit
labs(title = "Percentage of Women Jails vs. Readiness for Woman President",
x = "Percentage of Women in Jails",
y = "Percentage Ready for Woman President") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 15 rows containing missing values or values outside the scale range
## (`geom_point()`).
# nothing significant here